The manufacture and usage of photovoltaic systems has advanced significantly in recent years due to a continually growing demand for renewable energy resources. The cost per watt of electricity generated by photovoltaic systems has decreased dramatically, especially when combined with government incentives offered to encourage photovoltaic power generation. Photovoltaic systems are widely applicable as standalone off-grid power systems, sources of supplemental electricity, such as for use in a building or house, and as power grid-connected systems. Typically, when integrated into a power grid, photovoltaic systems are collectively operated as a fleet, although the individual systems in the fleet may be deployed at different physical locations within a geographic region.
Grid connection of photovoltaic power generation fleets is a fairly recent development. In the United States, the Energy Policy Act of 1992 deregulated power utilities and mandated the opening of access to power grids to outsiders, including independent power providers, electricity retailers, integrated energy companies, and Independent System Operators (ISOs) and Regional Transmission Organizations (RTOs). A power grid is an electricity generation, transmission, and distribution infrastructure that delivers electricity from supplies to consumers. As electricity is consumed almost immediately upon production, power generation and consumption must be balanced across the entire power grid. A large power failure in one part of the grid could cause electrical current to reroute from remaining power generators over transmission lines of insufficient capacity, which creates the possibility of cascading failures and widespread power outages.
As a result, planners and operators of power grids need to be able to accurately gauge both on-going and forecasted power generation and consumption. Photovoltaic fleets participating as part of a power grid are expected to exhibit predictable power generation behaviors. Power production data is needed at all levels of a power grid to which a photovoltaic fleet is connected. Accurate power production data is particularly crucial when a photovoltaic fleet makes a significant contribution to a power grid's overall energy mix. At the individual photovoltaic plant level, power production forecasting first involves obtaining a prediction of solar irradiance, which can be derived from ground-based measurements, satellite imagery, numerical weather prediction models, or other sources. The predicted solar irradiance data and each photovoltaic plant's system configuration is then combined with a photovoltaic simulation model, which generates a forecast of individual plant power output production. The individual photovoltaic plant forecasts can then be combined into a photovoltaic power generation fleet forecast, such as described in commonly-assigned U.S. Pat. Nos. 8,165,811; 8,165,812; 8,165,813, all issued to Hoff on Apr. 24, 2012; U.S. Pat. Nos. 8,326,535; 8,326,536, issued to Hoff on Dec. 4, 2012; and U.S. Pat. No. 8,335,649, issued to Hoff on Dec. 18, 2012, the disclosures of which are incorporated by reference.
A grid-connected photovoltaic fleet can be operationally dispersed over a neighborhood, utility region, or several states, and its constituent photovoltaic systems (or plants) may be concentrated together or spread out. Regardless, the aggregate grid power contribution of a photovoltaic fleet is determined as a function of the individual power contributions of its constituent photovoltaic plants, which, in turn, may have different system configurations and power capacities. Photovoltaic system configurations are critical to forecasting plant power output. Inaccuracies in the assumed specifications of photovoltaic system configurations directly translate to inaccuracies in their power output forecasts. Individual photovoltaic system configurations may vary based on power rating and electrical characteristics and by their operational features, such azimuth and tilt angles and shading or other physical obstructions.
Photovoltaic system power output is particularly sensitive to shading due to cloud cover, and a photovoltaic array with only a small portion covered in shade can suffer a dramatic decrease in power output. For a single photovoltaic system, power capacity is measured by the maximum power output determined under standard test conditions and is expressed in units of Watt peak (Wp). However, at any given time, the actual power could vary from the rated system power capacity depending upon geographic location, time of day, weather conditions, and other factors. Moreover, photovoltaic fleets that combine individual plants physically scattered over a large geographical area may be subject to different location-specific weather conditions due to cloud cover and cloud speed with a consequential affect on aggregate fleet power output.
Consequently, photovoltaic fleets operating under cloudy conditions, particularly when geographically dispersed, can exhibit variable and potentially unpredictable performance. Conventionally, fleet variability is determined by collecting and feeding direct power measurements from individual photovoltaic systems or equivalent indirectly-derived power measurements into a centralized control computer or similar arrangement. To be of optimal usefulness, the direct power measurement data must be collected in near real time at fine-grained time intervals to enable a high resolution time series of power output to be created. However, the practicality of such an approach diminishes as the number of systems, variations in system configurations, and geographic dispersion of the photovoltaic fleet grow. Moreover, the costs and feasibility of providing remote power measurement data can make high speed data collection and analysis insurmountable due to the bandwidth needed to transmit and the storage space needed to contain collected measurements, and the processing resources needed to scale quantitative power measurement analyses upwards as the fleet size grows.
For instance, one direct approach to obtaining high speed time series power production data from a fleet of existing photovoltaic systems is to install physical meters on every photovoltaic system, record the electrical power output at a desired time interval, such as every 10 seconds, and sum the recorded output across all photovoltaic systems in the fleet at each time interval. The totalized power data from the photovoltaic fleet could then be used to calculate the time-averaged fleet power, variance of fleet power, and similar values for the rate of change of fleet power. An equivalent direct approach to obtaining high speed time series power production data for a future photovoltaic fleet or an existing photovoltaic fleet with incomplete metering and telemetry is to collect solar irradiance data from a dense network of weather monitoring stations covering all anticipated locations of interest at the desired time interval, use a photovoltaic performance model to simulate the high speed time series output data for each photovoltaic system individually, and then sum the results at each time interval.
With either direct approach to obtaining high speed time series power production data, several difficulties arise. First, in terms of physical plant, calibrating, installing, operating, and maintaining meters and weather stations is expensive and detracts from cost savings otherwise afforded through a renewable energy source. Similarly, collecting, validating, transmitting, and storing high speed data for every photovoltaic system or location requires collateral data communications and processing infrastructure, again at possibly significant expense. Moreover, data loss occurs whenever instrumentation or data communications do not operate reliably.
Second, in terms of inherent limitations, both direct approaches to obtaining high speed time series power production data only work for times, locations, and photovoltaic system configurations when and where meters are pre-installed; thus, high speed time series power production data is unavailable for all other locations, time periods, and photovoltaic system configurations. Both direct approaches also cannot be used to directly forecast future photovoltaic system performance since meters must be physically present at the time and location of interest. Fundamentally, data also must be recorded at the time resolution that corresponds to the desired output time resolution. While low time-resolution results can be calculated from high resolution data, the opposite calculation is not possible. For example, photovoltaic fleet behavior with a 10-second resolution can not be determined from data collected by existing utility meters that collect the data with a 15-minute resolution.
The few solar data networks that exist in the United States, such as the ARM network, described in G. M. Stokes et al., “The atmospheric radiation measurement (ARM) program: programmatic background and design of the cloud and radiation test bed,” Bulletin of Am. Meteor. Soc., Vol. 75, pp. 1201-1221 (1994), the disclosure of which is incorporated by reference, and the SURFRAD network, do not have high density networks (the closest pair of stations in the ARM network are 50 km apart) nor have they been collecting data at a fast rate (the fastest rate is 20 seconds in the ARM network and one minute in the SURFRAD network). The limitations of the direct measurement approaches have prompted researchers to evaluate other alternatives. Researchers have installed dense networks of solar monitoring devices in a few limited locations, such as described in S. Kuszamaul et al., “Lanai High-Density Irradiance Sensor Network for Characterizing Solar Resource Variability of MW-Scale PV System.” 35th Photovoltaic Specialists Conf., Honolulu, Hi. (Jun. 20-25, 2010), and R. George, “Estimating Ramp Rates for Large PV Systems Using a Dense Array of Measured Solar Radiation Data,” Am. Solar Energy Society Annual Conf. Procs., Raleigh, N.C. (May 18, 2011), the disclosures of which are incorporated by reference. As data are being collected, the researchers examine the data to determine if there are underlying models that can translate results from these devices to photovoltaic fleet production at a much broader area, yet fail to provide translation of the data. In addition, half-hour or hourly satellite irradiance data for specific locations and time periods of interest have been combined with randomly selected high speed data from a limited number of ground-based weather stations, such as described in CAISO 2011. “Summary of Preliminary Results of 33% Renewable Integration Study—2010,” Cal. Public Util. Comm. LTPP No. R.10-05-006 (Apr. 29, 2011) and J. Stein, “Simulation of 1-Minute Power Output from Utility-Scale Photovoltaic Generation Systems,” Am. Solar Energy Society Annual Conf. Procs., Raleigh, N.C. (May 18, 2011), the disclosures of which are incorporated by reference. This approach, however, does not produce time synchronized photovoltaic fleet variability for any particular time period because the locations of the ground-based weather stations differ from the actual locations of the fleet. While such results may be useful as input data to photovoltaic simulation models for purpose of performing high penetration photovoltaic studies, they are not designed to produce data that could be used in grid operational tools.
Many of the concerns relating to high speed time series power production data acquisition also apply to photovoltaic fleet output estimation. Creating a fleet power forecast requires evaluation of the solar irradiance expected over each location within a photovoltaic fleet, which must be inferred for those locations where measurement-based sources of historical solar irradiance data are lacking. Moreover, solar irradiance data derived from satellite imagery requires additional processing prior to use in simulating fleet time series output data. Satellite imagery is considered to be a set of area values that must be converted into point values by first determining area solar irradiance statistics followed by finding point irradiance statistics as correlated across the individual plant locations within the fleet. The correlation of the point statistics, though, requires finding solutions in a problem space that grows exponentially with the number of locations. For example, a fleet with 10,000 photovoltaic systems would require the computation of a correlation coefficient matrix with 100 million calculations. Accordingly, the determination of inter-fleet correlation and expected fleet power, especially in the near term, becomes computationally impracticable as the fleet's size increases. Therefore, a need remains for an approach to efficiently correlating solar irradiance statistics across a plurality of photovoltaic locations for use in forecasting fleet power output.